On Sun, Jan 22, 2012 at 3:34 PM, Andreas <[email protected]> wrote: > Hi everybody. > While reviewing the label propagation PR, I thought about the pairwise > rbf functions. > Would it be possible to compute an sparse, approximate RBF kernel matrix > using ball trees? > The idea would be that if the distance between two points is some > "large" multiple of gamma, the kernel can be assumed > to be zero. > Do you think this is feasible to implement and helpful for real data?
I don't know how you would implement it but I think this would be interesting. rbf in scipy is running into memory problems if the number of observations is too large. I tried a version once that used scipy kdtree to build a sparse distance/kernel matrix. Josef > > Cheers, > Andy > > ------------------------------------------------------------------------------ > Try before you buy = See our experts in action! > The most comprehensive online learning library for Microsoft developers > is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, > Metro Style Apps, more. Free future releases when you subscribe now! > http://p.sf.net/sfu/learndevnow-dev2 > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Try before you buy = See our experts in action! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
